Low impact crash detection for a vehicle

ABSTRACT

Systems and methods for detecting low impact collisions for a vehicle ( 100 ). The system includes at least one sensor ( 99, 110, 111, 115, 120 - 123, 125 - 136, 140, 141 ) and an electronic controller ( 150 ). The electronic controller ( 150 ) is configured to receive sensor data from the sensor ( 99, 110, 111, 115, 120 - 123, 125 - 136, 140, 141 ) and determine one or more features of the sensor data received from the at least one sensor. The electronic controller ( 150 ) is further configured to determine if a collision has occurred based upon the one or more features of the sensor data, and take at least one action in response to determining that the collision has occurred.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/754,299 filed Nov. 1, 2018 and U.S. Provisional PatentApplication No. 62/808,149 filed Feb. 20, 2019, the entire contents ofwhich are incorporated by reference in their entirety.

FIELD

Embodiments relate to systems and methods for low impact crash detectionfor a vehicle.

BACKGROUND

In some countries, such as the United States, drivers are required underpenalty of law to report accidents that result in injury or propertydamage, even if the injury or damage is not severe. Additionally, if thevehicle is an autonomous vehicle, the vehicle must be stopped until theaccident is reported and handled by the proper authorities.

Current vehicle passive safety systems (for example, sensors andassociated computers or electronic control units) to detect vehiclecollisions or other safety hazards) do not have the capability to detectlow impact or non-severe accidents, which creates problems for driverswho do not notice the impact or autonomous vehicles equipped with thesesystems. For example, current crash sensing systems for vehicles canonly detect major collisions resulting in a large amount of damage, andnot minor collisions (such as a bike running into a vehicle, a bumper ofthe vehicle gently tapping a road sign, a pedestrian hit by the vehicle,and the like).

SUMMARY

Therefore, a system is provided for detecting low impact crashes for avehicle (such as a bike running into a vehicle, a bumper of the vehiclegently tapping a road sign, a pedestrian hit by the vehicle, and thelike).

One embodiment provides a system for detecting low impact crashes for avehicle. The system includes at least one sensor, and an electroniccontroller configured to receive sensor data from the sensor, determineone or more features of the sensor data received from the at least onesensor, determine if a collision has occurred based upon the one or morefeatures of the sensor data, and take at least one action in response todetermining that the collision has occurred.

Another embodiment provides a method for detecting low-impact collisionsfor a vehicle. The method includes receiving, with an electroniccontroller, sensor data from at least one sensor and determining, withthe electronic controller, one or more features of the sensor datareceived from the at least one sensor. The method further includesdetermining, with the electronic controller, if a collision has occurredbased upon the one or more features of the sensor data, and taking, withthe electronic controller, at least one action in response todetermining that the collision has occurred.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for detecting low impact crashes for avehicle according to one embodiment.

FIG. 2 illustrates an electronic controller according to one embodiment.

FIG. 3 is a block diagram illustrating software logic according to oneembodiment.

FIG. 4 illustrates a method of detecting low-impact collisions for avehicle according to one embodiment.

FIG. 5 illustrates characteristics of different low-impact collisionsaccording to one embodiment.

FIG. 6 illustrates an example of a Bayes classifier being used todetermine if a collision has occurred.

FIG. 7A illustrates a first configuration of sensors according to oneembodiment.

FIG. 7B illustrates a first chart illustrating the detection of impactsignals according to one embodiment.

FIG. 7C illustrates a continuation of the first chart according to theembodiment of FIG. 7B.

FIG. 8A illustrates a second configuration of sensors according to oneembodiment.

FIG. 8B illustrates a second chart illustrating the detection of impactsignals according to one embodiment.

FIG. 8C illustrates a continuation of the second chart according to theembodiment of FIG. 8B.

FIG. 9A illustrates a third configuration of sensors according to oneembodiment.

FIG. 9B illustrates a third chart illustrating the detection of impactsignals according to one embodiment.

FIG. 9C illustrates a continuation of the third chart according to theembodiment of FIG. 9B.

FIG. 10 illustrates external noise versus a low impact audio signalaccording to one embodiment.

FIG. 11 illustrates detection of a low impact collision amidst externalnoise according to one embodiment.

DETAILED DESCRIPTION

Before any embodiments are explained in detail, it is to be understoodthat this disclosure is not intended to be limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.Embodiments are capable of other configurations and of being practicedor of being carried out in various ways.

A plurality of hardware and software based devices, as well as aplurality of different structural components may be used to implementvarious embodiments. In addition, embodiments may include hardware,software, and electronic components or modules that, for purposes ofdiscussion, may be illustrated and described as if the majority of thecomponents were implemented solely in hardware. However, one of ordinaryskill in the art, and based on a reading of this detailed description,would recognize that, in at least one embodiment, the electronic basedaspects of the invention may be implemented in software (for example,stored on non-transitory computer-readable medium) executable by one ormore processors. For example, “control units” and “controllers”described in the specification can include one or more electronicprocessors, one or more memory modules including non-transitorycomputer-readable medium, one or more input/output interfaces, one ormore application specific integrated circuits (ASICs), and variousconnections (for example, a system bus) connecting the variouscomponents.

In addition, the functionality described herein as being performed byone component may be performed by multiple components in a distributedmanner. Likewise, functionality performed by multiple components may beconsolidated and performed by a single component. Similarly, a componentdescribed as performing particular functionality may also performadditional functionality not described herein. For example, a device orstructure that is “configured” in a certain way is configured in atleast that way, but may also be configured in ways that are not listed.Furthermore, some embodiments described herein may include one or moreelectronic processors configured to perform the described functionalityby executing instructions stored in non-transitory, computer-readablemedium. Similarly, embodiments described herein may be implemented asnon-transitory, computer-readable medium storing instructions executableby one or more electronic processors to perform the describedfunctionality. As used in the present application, “non-transitorycomputer-readable medium” comprises all computer-readable media but doesnot consist of a transitory, propagating signal. Accordingly,non-transitory computer-readable medium may include, for example, a harddisk, flash memory, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a RAM (Random Access Memory), registermemory, a processor cache, or any combination thereof.

FIG. 1 illustrates a system 100 for detecting low impact crashes for avehicle 105 according to one embodiment. The system 100 includes one ormore exterior microphones 110 and 111, an interior microphone 115, oneor more road noise sensors 120-123, one or more acceleration sensors125-136, and one or more pressure sensors 140 and 141.

The vehicle 105 may be an automobile, motorcycle, tractor-trailer,truck, van, and the like. The exemplary embodiment of the vehicle 105shown in FIG. 1 includes four wheels. However, other embodiments of thevehicle 105 may include less wheels (for example, a motorcycle on twowheels) or more wheels (such as a tractor-trailer with multiple wheelsper axle).

The one or more exterior microphones 110 and 111 are configured togather audio data outside the vehicle 105, for example, airborne or bodysounds. For example, the one or more exterior microphones 110 and 111gather audio data of objects impacting the vehicle 105. The one or moreexterior microphones 110 and 111 are located, for example, at a licenseplate area on a front portion of the vehicle 105 and at a second licenseplate area located on a rear portion of the vehicle 105.

The interior microphone 115 is configured to gather audio data insidethe vehicle 105. For example, the interior microphone 115 gathers audiodata inside the vehicle 105 as an object strikes the vehicle 105. Theinterior microphone 115 is located, for example, on a rearview mirrorinside the vehicle 105.

The one or more road noise sensors 120-123 are configured to utilize anaccelerometer to gather low g-force data introduced by a driving surfacethat the vehicle 105 is driving on into the body of the vehicle 105 viawheels of the vehicle 105. For example, the road noise sensors 120-123are located on or near axles of the vehicle 105 and detect low g-forcedata of the wheels of the vehicle 105 contacting the driving surface orother driving obstacles on the driving surface (such as speed bumps,debris, and the like).

The one or more acceleration sensors 125-136 may be, for example, aninertial sensor configured to measure acceleration in one or more axesof movement. The one or more acceleration sensors 125-136 may alsoinclude a gyroscope to measure angular velocity. The one or moreacceleration sensors 125-136 measure an acceleration and/or angularvelocity of the vehicle 105 or portions of the vehicle 105 (such as aside panel, a bumper, and the like) that occur in response to acollision with an object. The one or more acceleration sensors 125-136may be configured to detect low g-force accelerations, such as impactsof 2 to 16 kilometers per hour (for example, a pedestrian colliding witha side of the vehicle 105). In some embodiments, the one or moreacceleration sensors 125-136 may include a variety of sensors configuredto detect different levels of g-force from impacts. In one example, thesensors are configured to detect low g-force impacts (such as the onesdescribed above) and mid g-force impacts. Mid g-force impacts mayresults from objects traveling above 16 kilometers per hour collidingwith the vehicle (for example, a motorcycle colliding with a side of thevehicle 105). In one instance, the 6D cluster sensors are used to detectmid g-force impacts.

The one or more pressure sensors 140 and 141 are configured to detectpressure being applied on portions of the vehicle 105. For example, theone or more pressure sensors 140 and 141 may be located on side doors ofthe vehicle 105 and configured to measure pressure applied to the sidedoors of the vehicle.

It is to be understood that the one or more exterior microphones 110 and111, the interior microphone 115, the one or more road noise sensors120-123, the one or more acceleration sensors 125-136, and the one ormore pressure sensors 140 and 141 may be located at or on any portion ofthe vehicle 105, and that the locations of each provided in FIG. 1 arepart of an exemplary embodiment of the system 100.

The one or more exterior microphones 110 and 111, the interiormicrophone 115, the one or more road noise sensors 120-123, the one ormore acceleration sensors 125-136, and the one or more pressure sensors140 and 141 are electrically connected to an electronic controller 150and are configured to send data to the electronic controller 150. Anembodiment of the electronic controller 150 is illustrated in FIG. 2.

The electronic controller 150 includes a plurality of electrical andelectronic components that provide power, operation control, andprotection to the components and modules within the electroniccontroller 150. In the example illustrated, the electronic controller150 includes an electronic processor 205 (such as a programmableelectronic microprocessor, microcontroller, or similar device), a memory210 (for example, non-transitory, computer-readable memory), and aninput-output interface 215. The electronic processor 205 iscommunicatively connected to the memory 210 and the input-outputinterface 215. The electronic processor 205, in coordination withsoftware stored in the memory 210 and the input-output interface 215, isconfigured to implement, among other things, methods described herein.

The electronic controller 150, in some embodiments, may be implementedin several independent controllers (for example, programmable electroniccontrol units) each configured to perform specific functions orsub-functions. Additionally, the electronic controller 130 may containsub-modules that include additional electronic processors, memory, orapplication-specific integrated circuits (ASICs) for handlinginput-output functions, processing of signals, and application of themethods listed below. In other embodiments, the electronic controller130 includes additional, fewer, or different components.

The electronic controller 150 may also include an integrated 6D sensorcluster 220. The integrated 6D sensor cluster 220 includes, in oneembodiment, a 3D acceleration sensor, a 3D gyroscope, and a central 2Dbody sound sensor. The central 2D body sound sensor detectsaccelerations introduced into a body of the vehicle 105 by differentforces (such as doors opening and closing, from a driving surface

FIG. 3 is a block diagram illustrating a portion of software logic 300for the system 100 according to one embodiment. The electroniccontroller 150 receives sensor data 305 from a sensor (for example, theone or more acceleration sensors 125-136 or the one or more pressuresensors 140 and 141), one or more vehicle parameters 307 received from astandalone sensor cluster or integrated in the electronic controller 150(such as yaw, pitch, roll, acceleration in x, y, z axis), and audio data310 and 311 from the one or more exterior microphones 110 and 111 (audiodata 310) and the interior microphone 115 (audio data 311). Theelectronic controller 150 uses these four inputs 315-318 to performcontact detection using contact detection software 320 as describedbelow. If contact is detected based upon the four inputs 315-318, theelectronic controller 150 is configured to take at least one action (at325).

The electronic controller 150 may also include extra plausibility stepsoftware. The extra plausibility step software includes instructions toprocess the sensor data 305, vehicle parameters 307, and audio data 310and 311 to remove outlier data or perform an initial comparison of thesensor data 305, vehicle parameters 307, and/or audio data 310 and 311to known collision data characteristics, or otherwise process the sensordata 305, the vehicle parameters 307, and the audio data 310 and 311.For example, the electronic controller 150 may receive data from the oneor more acceleration sensors 125-136 indicating an accelerationindicative of a collision, but the one or more road sensors 120-123 mayhelp filter out the acceleration as noise from a pothole or a roughroad, which would not be considered a low-impact collision causinginjury or damage. In another embodiment, the one or more accelerationsensors 125-136 may detect a door slam as an acceleration, but theelectronic controller 150 may receive data from a secondary electroniccontroller indicating a door was shut (for example, receiving a dataflag from the secondary electronic controller indicating a Boolean valuefor door open, for example, 0 being false and 1 being true), and filterthe detected sound out as a door slam instead of a collision.

In another embodiment, the one or more acceleration sensors 125-136 maydetect a door slam as an acceleration, but the electronic controller 150may receive data from the secondary electronic controller indicating aproximity of an object to the vehicle (for example, data from anultrasonic sensor system or video data from a video system including oneor more cameras mounted on the vehicle 105). Based upon the proximity ofthe object, the electronic controller 150 is configured to increase ordecrease a sensitivity of the contact detection software 320. Forexample, if the electronic controller 150 determines that a secondvehicle is in close proximity to the vehicle 105, the electroniccontroller 150 will increase sensitivity by not filtering out a doorslam (during, for example, operation of the extra plausibility stepsoftware). In this case, the door slam may be a door of the vehicle 105impacting the second vehicle, which is a low-impact collision. Incontrast, if no object is in close proximity to the vehicle 105, theelectronic controller 150 may decrease the sensitivity of the contactdetection software 320 to ignore all sensor data indicative of a doorslam, as the sensor data indicating the door slam will only be a doorclosing on the vehicle 105. It is to be understood that the filtering ofa door slam is only an example and that data from other sensorsdescribed in this application or from other vehicle systems could beused to increase or decrease the sensitivity of the contact detectionsoftware 320.

The contact detection software 320 and the extra plausibility stepsoftware may be stored in the memory 210.

FIG. 4 illustrates a method 400 of detecting low-impact collisions forthe vehicle 105 according to one embodiment and implemented, forexample, by the contact detection software 320. The method 400 includesreceiving, with the electronic controller 150, data from at least onesensor (at block 405). For example, the electronic controller 150 mayreceive data from the one or more external microphones 110 and 111, theinternal microphone 115, the one or more road noise sensors 120-123, theone or more acceleration sensors 125-136, the one or more pressuresensors 140 and 141, or any combination of these.

In some embodiments, audio data from the one or more externalmicrophones 110 and 111 and/or the internal microphone 115 oracceleration data from the one or more road noise sensors 120-123 isused only to validate or filter other sensor data received from adifferent sensor (as described below with regards to a plausibilitystep). In other embodiments, any impact detected as an audible noise bythe one or more external microphones 110 and 111 and/or the internalmicrophone 115 is used (either alone or in conjunction with other sensordata) to confirm that a collision has occurred.

The method 400 also includes performing, with the electronic controller150, a plausibility step on the received sensor data (at block 410). Asdescribed above with regards to the extra plausibility step software,the plausibility step is used to filter out unwanted data that could bemisinterpreted as a low-impact collision. For example, as discussedabove, the vehicle 105 may drive over a rough patch of road, and the oneor more acceleration sensors 125-136 may detect an acceleration. Whenthe electronic controller 150 receives this data, instead of immediatelyusing it to determine if a low-impact collision has occurred, theelectronic controller 150 utilizes the extra plausibility step softwareto filter out the noise of the rough patch of road (for example, byusing data from the one or more road noise sensors 120-123). In thisway, false positives can be avoided.

The method 400 also includes determining, with the electronic controller150, one or more features of the sensor data (at block 415). Forexample, the electronic controller 150 may determine an amplitude of thesensor data, determine a signal energy of the sensor data, determine oneor more frequencies of the sensor data, perform a Fourier transform onthe sensor data to obtain a frequency representation of the signal (asopposed to a time representation of the signal), determine an amount anddirection of acceleration, determine an angular rotation, and the like.

In the example provided, the method 400 also includes determining, withthe electronic controller 150, if a collision has occurred (at block420). In order to determine that a collision has occurred, theelectronic controller 150 may, in some embodiments, compare thedetermined features of the sensor data (from block 415) to knowncharacteristics of different impacts. The known characteristics may bedata sets stored in the memory 210. For example, an impact by an objectat 3 kilometers per hour on a door of the vehicle 105 may have a knownamplitude, set of frequencies, amount of acceleration, and the like. Ifthe determined features match the known characteristics of this impact,the electronic controller 150 determines that a collision has occurred.

For example, FIG. 5 illustrates characteristics of different low-impactcollisions 500 according to one embodiment. Columns 505, 510, and 515illustrate known low-impact collisions (knocking on a door of thevehicle 105, a shopping cart rolling into the vehicle 105, and a screwdriver scratching on a door of the vehicle 105, respectively), whilerows 520, 525, and 530 illustrate the characteristics of the knownlow-impact collisions (acceleration in g-force, rotation rate of agyroscope, and pressure, respectively). The electronic controller 150compares the received sensor data to known characteristics and, if thesensor data matches a known low-impact collision to a threshold degreein one or more characteristics, the electronic controller 150 determinesthat a collision has occurred.

In other embodiments, the electronic controller 150 may utilize machinelearning to determine if a collision has occurred. For example, theelectronic controller 150 may utilize a Bayes classifier with a kernelfunction. A Bayes classifier (or Bayesian classifier) is a type ofprobabilistic classifier that predicts, given the input (the determinedfeatures), a set of probabilities that different events occurred(instead of, for example, outputting the most likely event). The Bayesclassifier utilizes Bayes' Theorem with strong independence assumptionsof the input features. The kernel function helps focus the Bayesclassifier by finding relationships between data points in data sets.The determined features are input into the Bayes classifier and, basedupon the kernel function and the input features, the electroniccontroller 150 outputs a set of probabilities that different eventsoccurred. For example, the electronic controller 150 may output that itis 75 percent likely that damage has occurred, 23 percent likely thatcontact but no damage has occurred, and 2 percent likely that no contacthas occurred based upon the inputs being processed. A Bayes classifiermay also be used to determine a location on the vehicle that damageoccurred and a type of contact or damage that has occurred. FIG. 6illustrates an example 600 of a Bayes classifier being used to determineif a collision has occurred (605), a location of the collision (610),and a type of the collision (615).

Alternatively, the electronic controller 150 may use a neural network todetermine if a collision has occurred. The neural network is trained byfeeding training data containing a number features into the networkalong with the outcome. The neural network has one or more nodes thatprocess the various input features to determine an outcome, which iscompared with the actual outcome to determine accuracy, and then theresult of the comparison (for example, correctly identified orincorrectly identified) is back-propagated through the network tocorrect for any errors in the node calculations. Over iterations of timeand with large, varied sets of training data (for example, a largevariety of accelerations, sounds, pressures, road noises, and the like),the neural network becomes more accurate in predicting if damage occurs,the location of the damage, and the type of damage that occurs. Afterthe neural network is trained (which may be done based upon factorytests and input into the memory 210 of the electronic controller 150),the electronic controller 150 is configured to determine the features ofthe sensor data (at block 415) and input the determined features intothe neural network to receive an output indicating that damage has orhas not occurred.

If the electronic controller 150 determines that damage has not occurred(at block 420), the method 400 returns to waiting to receive sensor data(at block 405). If the electronic controller 150 determines that damagehas occurred, the electronic controller 150 takes at least one action inresponse (at block 425). For example, the electronic controller 150 maybe configured to output a signal to a display in the vehicle 105indicating that damage has occurred (for example, output an indicationthat damage has occurred). If the collision is severe enough (forexample, the acceleration is above a threshold based upon a location),the electronic controller 150 may be configured to store additional data(for example, video data from one or more cameras on the vehicle 105) inthe memory 210 or a separate memory, such as an event data recordermemory, located within the electronic controller 150 or in a separateelectronic controller.

If the vehicle 105 is an autonomous vehicle, the electronic controller150 may be configured to transmit a notification of damage to a remotelocation (such as an insurance company for a claim, to a policedepartment, to a car dealership, to a repair facility, and the like)using a transceiver antenna, or another wireless communication device,send a signal (or a command) to a driving controller to slow the vehicle105 or stop the vehicle 105, and the like. The electronic controller 150may also be configured to store any sensor data and associateddeterminations regarding low-impact collisions in the memory 210 forlater access by a technician or other user of the vehicle 105.

FIG. 7A illustrates a first sensor configuration 700 according to oneembodiment. The first sensor configuration 700 includes the electroniccontroller 150 and peripheral contact sensors 701-711. Sensors 701 and706 are located on at the left and right front center fascia of thefront bumper of the vehicle 105. Sensors 702 and 707 are located at theleft and right sides of the engine bay of the vehicle 105. Sensors 703and 708 are located at the left and right B pillars of the vehicle 105.Pillars (A-C normally from front to rear of the vehicle 105) arevertical supports for the window areas of the vehicle 105. Sensors 704and 709 are located at the left and right C pillars of the vehicle 105.Sensors 705 and 710 are located at the left and right rear corners ofthe rear bumper of the vehicle 105. Sensor 711 is located on the reartrunk of the vehicle 105. This first sensor configuration 700 coversmost use cases for detecting low impact collisions. By having sensorcoverage at all of the locations of the peripheral contact sensors701-711, low impacts can be accurately detected at all points of thevehicle 105. For example, first chart 715, as shown in FIG. 7B and FIG.7C, illustrates how impact signals are detected for different objects (abasketball, crash test dummies, and tire noise) at different impactspeeds in different impact locations on the vehicle 105 in the firstsensor configuration 700.

FIG. 8A illustrates a second sensor configuration 800 according to oneembodiment. The second sensor configuration 800 includes the electroniccontroller 150 and peripheral contact sensors 801-808. Sensors 801 and805 are located at the left and right front fascia of the front bumperof the vehicle 105. Sensors 802 and 806 are located at the left andright sides of the engine bay of the vehicle 105. Sensors 803 and 807are located at the left and right C pillars of the vehicle 105. Sensors804 and 808 are located at the left and right rear corners of the rearbumper of the vehicle 105. In the second sensor configuration, lowimpact collisions of 6 kilometers per hour are detected to a high degreeof accuracy while requiring less sensors than the first sensorconfiguration 700. For example, a second chart, as shown in FIG. 8B andFIG. 8C, illustrates how impact signals are detected for differentobjects (a basketball, crash test dummies, and tire noise) at differentimpact speeds in different impact locations on the vehicle 105 in thesecond sensor configuration 800.

FIG. 9A illustrates a third sensor configuration 900 according to oneembodiment. The third sensor configuration 900 includes the electroniccontroller 150 and harsh environment microphones 901-904. Microphone 901is located on the front bumper of the vehicle 105. Microphones 902 and903 are located on the left and right sides of the vehicle 105.Microphone 904 is located on the rear bumper of the vehicle 105. Theharsh environment microphones 901-904 detect most of the use cases forlow impact collisions, but cannot be used reliably considering audionoise spectrums from external noise, such as road noise. To better judgethe external microphone audio data, an artificial intelligencealgorithm, such as the above-described machine learning or Bayesclassification algorithms, may be used. A third chart, as shown in FIGS.9B and 9C, illustrates how impact signals are detected for differentobjects (a basketball, crash test dummies, and tire noise) at differentimpact speeds in different impact locations on the vehicle 105 in thethird sensor configuration 900.

Separating real low impact contact from external noise, such as roadnoise, can be somewhat difficult. However, by comparing the energywithin different frequency windows (spectrograms) of the signal, lowimpact contact can be discerned from external noise. For example, inFIG. 10, the external (observed) noise of a double lane change at 50miles per hour 1010 is measured by a sensor at the A pillar of thevehicle 105. A low impact collision (2 kilometers per hour) on the sideof the vehicle 105 is also measured by the sensor at the A pillar(signal 1020). As shown, by comparing the energy of the differentfrequency windows, the low impact collision can be detected independentof the external noise. For example, FIG. 11 illustrates an area 1110where a low impact collision is detected amidst external noise. Thespike in frequency amidst the external noise indicates is determined tobe the low impact collision by the electronic controller 150.

The following examples illustrate example systems and methods describedherein. Example 1: A system for detecting low impact collisions for avehicle, the system comprising at least one sensor and an electroniccontroller configured to receive sensor data from the sensor, determineone or more features of the sensor data received from the at least onesensor, determine if a collision has occurred based upon the one or morefeatures of the sensor data, and take at least one action in response todetermining that the collision has occurred.

Example 2: the system of example 1, wherein the one or more features ofthe sensor data include an energy from one or more spectrograms of thesensor data.

Example 3: the system of any of examples 1-2, wherein the at least onesensor is one of a plurality of sensors, and wherein the plurality ofsensors includes a plurality of peripheral contact sensors.

Example 4: The system of any of examples 1-3, wherein the at least onesensor is one of a plurality of sensors, and wherein the plurality ofsensors includes a plurality of microphones.

Example 5: the system of any of examples 1-4, wherein the electroniccontroller is configured to determine if the collision has occurredusing a machine learning algorithm.

Example 6: the system of any of examples 1-4 and example 5, wherein themachine learning algorithm is a Bayesian classifier with a kernelfunction.

Example 7: the system of any of examples 1-4 and example 5, wherein themachine learning algorithm is a neural network trained to detect thecollision based upon the one or more features of the sensor data.

Example 8: the system of any of examples 1-7, wherein the action is anaction selected from the group consisting of outputting an indication ofdamage to a display and storing the sensor data in a memory

Example 9: the system of any of examples 1-7, wherein the vehicle is anautonomous vehicle, and wherein the action is an action selected fromthe group consisting of transmitting a notification of damage to aremote location via a wireless transceiver and transmitting a command toslow or stop the vehicle to a driving controller of the vehicle.

Example 10: the system of any of examples 1-9, wherein the electroniccontroller is further configured to filter out unwanted data from thesensor data.

Example 11: a method for detecting low-impact collisions for a vehicle,the method comprising receiving, with an electronic controller, sensordata from at least one sensor, determining, with the electroniccontroller, one or more features of the sensor data received from the atleast one sensor, determining, with the electronic controller, if acollision has occurred based upon the one or more features of the sensordata, and taking, with the electronic controller, at least one action inresponse to determining that the collision has occurred.

Example 12: the method of example 11, wherein the one or more featuresof the sensor data include an energy from one or more spectrograms ofthe sensor data.

Example 13: the method of any of examples 11-12, wherein the at leastone sensor is one of a plurality of sensors, and wherein the pluralityof sensors includes a plurality of peripheral contact sensors.

Example 14: the method of any of examples example 11-13, wherein the atleast one sensor is one of a plurality of sensors, and wherein theplurality of sensors includes a plurality of microphones.

Example 15: the method of any of examples 11-14, further comprisingdetermining, with the electronic controller, if the collision hasoccurred using a machine learning algorithm.

Example 16: the method of any of examples 11-14 and 15, wherein themachine learning algorithm is a Bayesian classifier with a kernelfunction.

Example 17: the method of any of examples 11-14 and 15, wherein themachine learning algorithm is a neural network trained to detect thecollision based upon the one or more features of the sensor data.

Example 18: the method of any of examples 11-17, wherein the action isan action selected from the group consisting of outputting, with theelectronic controller, an indication of damage to a display and storing,with the electronic controller, the sensor data in a memory

Example 19: the method of any of examples 11-17, wherein the vehicle isan autonomous vehicle, and wherein the action is an action selected fromthe group consisting of transmitting, with the electronic controller, anotification of damage to a remote location via a wireless transceiverand transmitting, with the electronic controller, a command to slow orstop the vehicle to a driving controller of the vehicle.

Example 20: the method of any of examples 11-19, wherein furthercomprising filtering, with the electronic controller, unwanted data fromthe sensor data.

Thus, embodiments described herein provide, among other things, systemsand methods for detecting low-impact collisions for a vehicle.

What is claimed is:
 1. A system for detecting low impact collisions fora vehicle, the system comprising: at least one sensor, and an electroniccontroller configured to receive sensor data from the sensor, determineone or more features of the sensor data received from the at least onesensor, determine if a collision has occurred based upon the one or morefeatures of the sensor data, and take at least one action in response todetermining that the collision has occurred.
 2. The system of claim 1,wherein the one or more features of the sensor data include an energyfrom one or more spectrograms of the sensor data.
 3. The system of claim1, wherein the at least one sensor is one of a plurality of sensors, andwherein the plurality of sensors includes a plurality of peripheralcontact sensors.
 4. The system of claim 1, wherein the at least onesensor is one of a plurality of sensors, and wherein the plurality ofsensors includes a plurality of microphones.
 5. The system of claim 1,wherein the electronic controller is configured to determine if thecollision has occurred using a machine learning algorithm.
 6. The systemof claim 5, wherein the machine learning algorithm is a Bayesianclassifier with a kernel function.
 7. The system of claim 5, wherein themachine learning algorithm is a neural network trained to detect thecollision based upon the one or more features of the sensor data.
 8. Thesystem of claim 1, wherein the action is an action selected from thegroup consisting of outputting an indication of damage to a display andstoring the sensor data in a memory
 9. The system of claim 1, whereinthe vehicle is an autonomous vehicle, and wherein the action is anaction selected from the group consisting of transmitting a notificationof damage to a remote location via a wireless transceiver andtransmitting a command to slow or stop the vehicle to a drivingcontroller of the vehicle.
 10. The system of claim 1, wherein theelectronic controller is further configured to filter out unwanted datafrom the sensor data.
 11. A method for detecting low-impact collisionsfor a vehicle, the method comprising receiving, with an electroniccontroller, sensor data from at least one sensor, determining, with theelectronic controller, one or more features of the sensor data receivedfrom the at least one sensor, determining, with the electroniccontroller, if a collision has occurred based upon the one or morefeatures of the sensor data, and taking, with the electronic controller,at least one action in response to determining that the collision hasoccurred.
 12. The method of claim 11, wherein the one or more featuresof the sensor data include an energy from one or more spectrograms ofthe sensor data.
 13. The method of claim 11, wherein the at least onesensor is one of a plurality of sensors, and wherein the plurality ofsensors includes a plurality of peripheral contact sensors.
 14. Themethod of claim 11, wherein the at least one sensor is one of aplurality of sensors, and wherein the plurality of sensors includes aplurality of microphones.
 15. The method of claim 11, further comprisingdetermining, with the electronic controller, if the collision hasoccurred using a machine learning algorithm.
 16. The method of claim 15,wherein the machine learning algorithm is a Bayesian classifier with akernel function.
 17. The method of claim 15, wherein the machinelearning algorithm is a neural network trained to detect the collisionbased upon the one or more features of the sensor data.
 18. The methodof claim 11, wherein the action is an action selected from the groupconsisting of outputting, with the electronic controller, an indicationof damage to a display and storing, with the electronic controller, thesensor data in a memory
 19. The method of claim 11, wherein the vehicleis an autonomous vehicle, and wherein the action is an action selectedfrom the group consisting of transmitting, with the electroniccontroller, a notification of damage to a remote location via a wirelesstransceiver and transmitting, with the electronic controller, a commandto slow or stop the vehicle to a driving controller of the vehicle. 20.The method of claim 11, wherein further comprising filtering, with theelectronic controller, unwanted data from the sensor data.